Gwin, Rachel

Thursday, December 14, 2017

This week was our final lab exercise for the Special Topics course. We finished up our open source project by conducting a food desert study on data from an area of our choosing with QGIS, created a webmap for it using mapbox, and shared it publicly with leaflet. You can view my webmap for my project on the southern portion of Walton County here. We also had to create a powerpoint presentation complete with audio to share our process and the results of the study. I don't want to spoil the final results, so if you are interested in the learning the percent of southern Walton County residents that live in a food desert area, you can watch my powerpoint here.

Saturday, December 2, 2017

For this week's lab we worked more on QGIS, Mapbox, and Leaflet to create food desert data and maps with our chosen study area. I chose to complete this project on my hometown area, the South Walton area of Florida. I downloaded US Census Bureau Tiger data from FGDL for Walton County that was organized by the census tracts. There was not available data for grocery stores, so I created data for the grocery store point layer. I located all grocery stores within the study area on google maps and created point graphics on the map, then converted the graphics to a feature layer of the grocery store points to edit the symbology and export. I included all named grocery stores in the data as well as permanent farmers markets, and made sure to exclude convenience stores that did not sell a variety of fresh produce. The data includes all grocery store locations that I could find, but may not be entirely conclusive if there is a newer store that has not been established online yet. The data depicts census tracts where a food desert exists. This refers to areas where the nearest grocery store is over 1 mile away, limiting accessibility to fresh produce for individuals without reliable transportation. Most of the South Walton census tracts are food desert areas, with the exception of the tract in the most populated area, that has three grocery stores. I used data from my local area, and I did expect similar results. There are not many grocery stores or farmer’s markets in the central to northern part of the county. The Santa Rosa Beach area does have more access to grocery stores however, and was the only census tract that was not a food desert area.

Many map hosting websites use tiling to expedite the loading process of webmaps. This process refers to breaking the maps into separate squares or tiles so they load quicker and can hold more details than original maps were loaded all at once. This allows webmaps to be much more detailed and aesthetically pleasing without having to worry about download speed. This process allows us to include more plugins, a legend, and a geocoder and the map will still load quickly. It has been very interesting learning about map hosting and html code.

Monday, November 27, 2017

Our lab this week was a new adventure into coding as well as in using mapbox to create a webmap. First, we created a webmap using our food deserts and grocery stores layers and learned how to customize and symbolize the layers. We had to duplicate the food desert layer for each class and edit the data content and symbolization to mimic a graduated color symbolization that GIS mapping programs would create. We then learned some basics about writing html code language and what each line represents to host our food desert map online through leaflet. We edited existing code to show our map, a legend of the map, a circle in a non food desert area, a polygon within a food desert area, a label and marker for Pensacola, and a geocoder. To view my final map, click here. Overall it was slightly less daunting than I was expecting, but still had some intricate parts.

Friday, November 17, 2017

This week's lab was our first introduction to open sourced GIS programs. We explored QGIS and recreated the "Own Your Map" lab to learn the basic settings then furthered our knowledge by learning how to complete some analysis in QGIS. We were taught how to do basic clip analysis, use the selection tool, and use the layout options to create our final map outputs. We learned about food deserts, which are urban areas where the nearest grocery store is over 1 mile away, leading to a lack of fresh produce and healthy food options for many residents. We used QGIS to map the food desert areas in Southern Escambia county, and selected the area where we will be conducting our own food desert analysis. The QGIS program offers a nice free alternative to ArcGIS if funds are limited, but definitely has a few more glitches than ArcMap. It offers a wonderful color selection and it is nice to have the layout in a separate window, but some of the other features are much trickier than in ArcMap.

Wednesday, November 15, 2017

This week's lab taught us about how to conduct a supervised classification in ERDAS Imagine. We learned different ways to create spectral signatures in the AOI layer, including manually drawing the polygon as well as using the grow tool to grow the polygon from "seed' or from a certain pixel. We also learned how to examine the histograms and mean plot chart to see where the most spectral confusion existed and to determine which bands to use for the display. We completed the process for an example map before conducting our own supervised classification of an area in Germantown, Maryland. I enjoyed learning about this method, but believe that my skills need some refining as my roads signature was too close to my urban areas signature and more of the area was diverted to roads than what exists in the image.

Tuesday, November 7, 2017

This week's lab focused on conducting unsupervised classifications in ArcMap and ERDAS Imagine to do aerial photography interpretation. First we explored how to use the Iso Cluster and Maximum Likelihood Classification tools to conduct an unsupervised classification in ArcMap. The main focus of this week was exploring the unsupervised classification tool in ERDAS Imagine and reclassifying the unsupervised classification output of the University of West Florida's campus. We also learned how to use a myriad of tools to aid in reclassifying the pixels, such as swipe, flicker, blend, and highlight. Finally, we had to calculate the total acreage of each class and approximate the total amount of permeable and impermeable surfaces. This week was a very useful lesson in image classification that will be helpful with future projects.

Friday, November 3, 2017

This week we worked on the analyze portion of the Statistical Analysis of Meth Labs in West Virginia Lab. We learned about the ordinary least squares (OLS) tool and how it can compare a list of independent variables to a selected dependent variable to tell if they are statistically significant. The process of studying the statistics in the output report (pictured below) and removing insignificant variables one by one was a repetitive process that required a lot of patience. There are six main checks when working with this tool to see if the variables are significant or not. We had to study the probabilities, coefficients, VIFs, standard residual, Jarque-Bera, and adjusted r-squared values to determine which variables were statistically significant and if the model included any bias. We started by looking at the coefficiants, probabilities, and VIFs and removed the variable with the highest probabilities and VIFs with low coefficients before we ran the tool again. We repeated this process removing, and sometimes adding back (and maybe even removing again) different variables to achieve the highest Jarque-Bera and adjusted r-squared statistics to determine which variables were the most statistically significant to the dependent variable. Pictured above is a map depicting the standard residual results of the final model. The main goal was to have as many census tracts in the pale yellow shade as possible. Being about 10 years removed from any statistics classes I was a bit nervous for this lab, but I have definitely learned a lot about socio-economic statistics and saw a small section of the statistical analysis that ArcMap is capable of.